Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators

Addresses the cost of high-order differential operators in neural networks. Taylor-mode automatic differentiation and randomized tensor contractions estimate operators for applications such as physics-informed neural networks.

Addresses the cost of high-order differential operators in neural networks. Taylor-mode automatic differentiation and randomized tensor contractions estimate operators for applications such as physics-informed neural networks.

When reading the original paper, identify its problem, inputs, outputs, and methodological assumptions.

Addresses the cost of high-order differential operators in neural networks. Taylor-mode automatic differentiation and randomized tensor contractions estimate operators for applications such as physics-informed neural networks.

Check the baselines, datasets, evaluation protocol, and ablations in the original paper. This directory overview does not provide an independent evaluation or experimental results table.